A Daily Covid-19 Cases Prediction System using Data Mining and Machine Learning Algorithm

Policy Makers ◽

Diverse Range ◽

Using Data

The start of 2020 marked the beginning of the deadly COVID-19 pandemic caused by the novel SARS-COV-2 from Wuhan, China. As of the time of writing, the virus had infected over 150 million people worldwide and resulted in more than 3.5 million global deaths. Accurate future predictions made through machine learning algorithms can be very useful as a guide for hospitals and policy makers to make adequate preparations and enact effective policies to combat the pandemic. This paper carries out a two pronged approach to analyzing COVID-19. First, the model utilizes the feature significance of random forest regressor to select eight of the most significant predictors (date, new tests, weekly hospital admissions, population density, total tests, total deaths, location, and total cases) for predicting daily increases of Covid-19 cases, highlighting potential target areas in order to achieve efficient pandemic responses. Then it utilizes machine learning algorithms such as linear regression, polynomial regression, and random forest regression to make accurate predictions of daily COVID-19 cases using a combination of this diverse range of predictors and proved to be competent at generating predictions with reasonable accuracy.

Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records

10.1101/2021.05.11.443554 ◽

2021 ◽

Author(s):

Catherine Ollagnier ◽

Claudia Kasper ◽

Anna Wallenbeck ◽

Linda Keeling ◽

Siavash A Bigdeli

Keyword(s):

Machine Learning ◽

Random Forest ◽

Real Time ◽

Feeding Behaviour ◽

Learning Algorithm ◽

Learning Algorithms ◽

Tail Biting ◽

Testing Set

Tail biting is a detrimental behaviour that impacts the welfare and health of pigs. Early detection of tail biting precursor signs allows for preventive measures to be taken, thus avoiding the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (e.g., random forest, neural networks) were evaluated from daily feeding data collected from 65 pens originating from 2 herds of grower-finisher pigs (25-100kg), in which 27 tail biting events occurred. Data were divided into training and testing data, either by randomly splitting data into 75% (training set) and 25% (testing set), or by randomly selecting pens to constitute the testing set. The random forest algorithm was able to predict 70% of the upcoming events with an accuracy of 94%, when predicting events in pens for which it had previous data. The detection of events for unknown pens was less sensitive, and the neural network model was able to detect 14% of the upcoming events with an accuracy of 63%. A machine-learning algorithm based on ongoing data collection should be considered for implementation into automatic feeder systems for real time prediction of tail biting events.

Deducing of Optimal Machine Learning Algorithms for Heterogeneity

10.36227/techrxiv.17162147 ◽

2021 ◽

Author(s):

Omar Alfarisi ◽

Zeyar Aung ◽

Mohamed Sassi

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Learning Algorithms ◽

Synthetic Data ◽

Supervised Machine Learning ◽

Data Set ◽

Optimal Machine

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneity, we identified Random Forest, among others, to be the best algorithm.

Amino Acid Composition and Charge Based Prediction of Antisepsis Peptides by Random Forest Machine Learning Algorithm

10.1101/2021.09.26.461860 ◽

2021 ◽

Author(s):

Aayushi Rathore ◽

Anu Saini ◽

Navjot Kaur ◽

Aparna Singh ◽

Ojasvi Dutta ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Learning Algorithms ◽

Multiple Organ ◽

The Body ◽

Support Vector ◽

Data Set ◽

Organ Systems

ABSTRACTSepsis is a severe infectious disease with high mortality, and it occurs when chemicals released in the bloodstream to fight an infection trigger inflammation throughout the body and it can cause a cascade of changes that damage multiple organ systems, leading them to fail, even resulting in death. In order to reduce the possibility of sepsis or infection antiseptics are used and process is known as antisepsis. Antiseptic peptides (ASPs) show properties similar to antigram-negative peptides, antigram-positive peptides and many more. Machine learning algorithms are useful in screening and identification of therapeutic peptides and thus provide initial filters or built confidence before using time consuming and laborious experimental approaches. In this study, various machine learning algorithms like Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbour (KNN) and Logistic Regression (LR) were evaluated for prediction of ASPs. Moreover, the characteristics physicochemical features of ASPs were also explored to use them in machine learning. Both manual and automatic feature selection methodology was employed to achieve best performance of machine learning algorithms. A 5-fold cross validation and independent data set validation proved RF as the best model for prediction of ASPs. Our RF model showed an accuracy of 97%, Matthew’s Correlation Coefficient (MCC) of 0.93, which are indication of a robust and good model. To our knowledge this is the first attempt to build a machine learning classifier for prediction of ASPs.

Spatial Simulation Modeling of Settlement Distribution Driven by Random Forest: Consideration of Landscape Visibility

Sustainability ◽

10.3390/su12114748 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4748

Author(s):

Minrui Zheng ◽

Wenwu Tang ◽

Akinwumi Ogundiran ◽

Jianxin Yang

Keyword(s):

Machine Learning ◽

Random Forest ◽

Simulation Modeling ◽

Learning Algorithm ◽

Learning Algorithms ◽

Simulation Models ◽

Spatial Simulation ◽

Study Region ◽

The Relationship

Settlement models help to understand the social–ecological functioning of landscape and associated land use and land cover change. One of the issues of settlement modeling is that models are typically used to explore the relationship between settlement locations and associated influential factors (e.g., slope and aspect). However, few studies in settlement modeling adopted landscape visibility analysis. Landscape visibility provides useful information for understanding human decision-making associated with the establishment of settlements. In the past years, machine learning algorithms have demonstrated their capabilities in improving the performance of the settlement modeling and particularly capturing the nonlinear relationship between settlement locations and their drivers. However, simulation models using machine learning algorithms in settlement modeling are still not well studied. Moreover, overfitting issues and optimization of model parameters are major challenges for most machine learning algorithms. Therefore, in this study, we sought to pursue two research objectives. First, we aimed to evaluate the contribution of viewsheds and landscape visibility to the simulation modeling of - settlement locations. The second objective is to examine the performance of the machine learning algorithm-based simulation models for settlement location studies. Our study region is located in the metropolitan area of Oyo Empire, Nigeria, West Africa, ca. AD 1570–1830, and its pre-Imperial antecedents, ca. AD 1360–1570. We developed an event-driven spatial simulation model enabled by random forest algorithm to represent dynamics in settlement systems in our study region. Experimental results demonstrate that viewsheds and landscape visibility may offer more insights into unveiling the underlying mechanism that drives settlement locations. Random forest algorithm, as a machine learning algorithm, provide solid support for establishing the relationship between settlement occurrences and their drivers.

Deducing of Optimal Machine Learning Algorithms for Heterogeneity

10.36227/techrxiv.17162147.v1 ◽

2021 ◽

Author(s):

Omar Alfarisi ◽

Zeyar Aung ◽

Mohamed Sassi

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithm ◽

Learning Algorithms ◽

Synthetic Data ◽

Supervised Machine Learning ◽

Data Set ◽

Optimal Machine

Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

A Robust Method to Predict Fluid Properties Based on Big Data and Machine Learning Algorithms

10.2523/iptc-21356-ms ◽

2021 ◽

Author(s):

Yingxian Liu ◽

Cunliang Chen ◽

Hanqing Zhao ◽

Yu Wang ◽

Xiaodong Han

Keyword(s):

Machine Learning ◽

Physical Properties ◽

Learning Algorithm ◽

Direct Method ◽

Learning Algorithms ◽

Small Error ◽

Well Test ◽

Empirical Formulas ◽

Fluid Properties

Abstract Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is underground sampling. However, not every well has samples due to technical reasons such as excessive well deviation or high cost during the exploration stage. Therefore, analogies or empirical formulas have to be adopted to carry out research in many cases. But a large number of oilfield developments have shown that the errors caused by these methods are very large. Therefore, how to quickly and accurately obtain fluid physical properties is of great significance. In recent years, with the development and improvement of artificial intelligence or machine learning algorithms, their applications in the oilfield have become more and more extensive. This paper proposed a method for predicting crude oil physical properties based on machine learning algorithms. This method uses PVT data from nearly 100 wells in Bohai Oilfield. 75% of the data is used for training and learning to obtain the prediction model, and the remaining 25% is used for testing. Practice shows that the prediction results of the machine learning algorithm are very close to the actual data, with a very small error. Finally, this method was used to apply the preliminary plan design of the BZ29 oilfield which is a new oilfield. Especially for the unsampled sand bodies, the fluid physical properties prediction was carried out. It also compares the influence of the analogy method on the scheme, which provides potential and risk analysis for scheme design. This method will be applied in more oil fields in the Bohai Sea in the future and has important promotion value.

Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽

10.3390/s18103532 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3532 ◽

Cited By ~ 16

Author(s):

Nicola Mansbridge ◽

Jurgen Mitsch ◽

Nicola Bollard ◽

Keith Ellis ◽

Giuliana Miguel-Pacheco ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Time Budget ◽

Learning Algorithms ◽

Eating Behaviour ◽

Support Vector ◽

Optimum Number ◽

Eating Behaviours ◽

Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

FLOOD MAPPING USING RANDOM FOREST AND IDENTIFYING THE ESSENTIAL CONDITIONING FACTORS; A CASE STUDY IN FREDERICTON, NEW BRUNSWICK, CANADA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2020-609-2020 ◽

2020 ◽

Vol V-3-2020 ◽

pp. 609-615 ◽

Cited By ~ 1

Author(s):

M. Esfandiari ◽

S. Jabari ◽

H. McGrath ◽

D. Coleman

Keyword(s):

Machine Learning ◽

Random Forest ◽

New Brunswick ◽

Urban Areas ◽

Learning Algorithm ◽

Satellite Image ◽

Slope Aspect ◽

Flood Peak ◽

Conditioning Factors

Abstract. Flood is one of the most damaging natural hazards in urban areas in many places around the world as well as the city of Fredericton, New Brunswick, Canada. Recently, Fredericton has been flooded in two consecutive years in 2018 and 2019. Due to the complicated behaviour of water when a river overflows its bank, estimating the flood extent is challenging. The issue gets even more challenging when several different factors are affecting the water flow, like the land texture or the surface flatness, with varying degrees of intensity. Recently, machine learning algorithms and statistical methods are being used in many research studies for generating flood susceptibility maps using topographical, hydrological, and geological conditioning factors. One of the major issues that researchers have been facing is the complexity and the number of features required to input in a machine-learning algorithm to produce acceptable results. In this research, we used Random Forest to model the 2018 flood in Fredericton and analyzed the effect of several combinations of 12 different flood conditioning factors. The factors were tested against a Sentinel-2 optical satellite image available around the flood peak day. The highest accuracy was obtained using only 5 factors namely, altitude, slope, aspect, distance from the river, and land-use/cover with 97.57% overall accuracy and 95.14% kappa coefficient.

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Risk Monitoring and Quantitative Results of Various Attributes of Machine Learning Algorithms with a Time Series Data

10.35940/ijitee.j9570.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 4018-4022

Keyword(s):

Machine Learning ◽

Time Series Data ◽

Learning Algorithm ◽

Learning Algorithms ◽

Series Data ◽

Risk Modelling ◽

Risk Monitoring ◽

Quantitative Results

The aim of this research is to do risk modelling after analysis of twitter posts based on certain sentiment analysis. In this research we analyze posts of several users or a particular user to check whether they can be cause of concern to the society or not. Every sentiment like happy, sad, anger and other emotions are going to provide scaling of severity in the conclusion of final table on which machine learning algorithm is applied. The data which is put under the machine learning algorithms are been monitored over a period of time and it is related to a particular topic in an area